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http://hdl.handle.net/10397/106181
Title: | Deep learning-based bluetooth low-energy 5.1 multianchor indoor positioning with attentional data filtering | Authors: | Lyu, Z Chan, TTL Hung, TYT Ji, H Leung, G Lun, DPK |
Issue Date: | Jan-2024 | Source: | Advanced intelligent systems, Jan. 2024, v. 6, no. 1, 2300292 | Abstract: | Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low-energy (BLE) 5.1 specification enables in-phase and quadrature-phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning-based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high-quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state-of-the-art approaches. Herein, a novel deep learning-based multianchor BLE 5.1 indoor positioning system is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, an attentional filtering network tailored to infer high-quality I/Q sample data is developed and spatial regularization loss incorporating spatial location relationships is proposed to strengthen the feature embedding discrimination. | Keywords: | Attention-based deep neural networks BLE 5.1 Data filtering Fingerprinting Indoor positioning Interferences |
Publisher: | Wiley-VCH Verlag GmbH & Co. KGaA | Journal: | Advanced intelligent systems | EISSN: | 2640-4567 | DOI: | 10.1002/aisy.202300292 | Rights: | © 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The following publication Lyu, Z., Chan, T.T., Hung, T.Y., Ji, H., Leung, G. and Lun, D.P. (2024), Deep Learning-Based Bluetooth Low-Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering. Adv. Intell. Syst., 6: 2300292 is available at https://dx.doi.org/10.1002/aisy.202300292. |
Appears in Collections: | Journal/Magazine Article |
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Lyu_Deep_Learning-Based_Bluetooth.pdf | 1.91 MB | Adobe PDF | View/Open |
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